Nuruzzaman Faruqui is a doctoral researcher in Data Science and Artificial Intelligence at Monash University in Melbourne, and an Assistant Professor at Daffodil International University in Bangladesh, currently on study leave. He has spent most of his career teaching programming and AI, in university classrooms and in online courses, and has twice received Daffodil International University’s Excellence in Teaching Award. His research covers adversarial machine learning, certified robustness, EEG and biosignals, and brain-inspired computing, and he has published more than fifty research papers and five books. He believes anyone can learn to program if it’s explained the way a good friend would explain it, and this book is his attempt to do exactly that.
Levin Kuhlmann is an Associate Professor in the Department of Data Science and AI, Faculty of Information Technology, Monash University, where he leads the brAIn Lab and previously directed the Master of Artificial Intelligence. He holds a PhD in Cognitive and Neural Systems from Boston University and spent several years as a research fellow at the University of Melbourne and St Vincent’s Hospital Melbourne. His work brings artificial intelligence to neuroscience, neurology and brain imaging, including seizure prediction, and has led to more than 140 publications. He has twice been named Monash Supervisor of the Year.
Mahsa Salehi is a Senior Lecturer and Deputy Director of Engagement in the Department of Data Science and AI at Monash University, where she teaches data science. She completed her PhD in machine learning at the University of Melbourne and worked as a postdoctoral research fellow with IBM Research before joining Monash. Her research focuses on time series analysis and anomaly detection, with more than 65 publications and several patents. She has received a Teaching Excellence Award from the Melbourne School of Engineering and a best paper runner-up award at the IEEE International Conference on Data Mining.
Yun Zhao is a Postdoctoral Research Associate at the University of Sydney and a part-time Research Fellow in the Department of Data Science and AI at Monash University, where he completed his PhD. His research sits where artificial intelligence, computational mathematics and biomedical imaging meet. He uses generative AI and Bayesian inference to improve positron emission tomography (PET) imaging, and statistical machine learning to model brain dynamics, study cortical stability and monitor focal seizures and changes in consciousness.